Each year, countless bikes disappear from Toronto’s streets, leaving cyclists frustrated and vulnerable. Our analysis digs into patterns and insights across the city, highlighting where and when bicycles are most at risk. With data spanning over a decade, we reveal theft hotspots and provide tips to help keep your bike safe.
Imagine locking up your bike on a bustling Toronto street, feeling reassured that a strong lock and some common sense will keep it safe. But what if certain seasons, specific bike types, or even certain neighborhoods increase your risk of theft far more than you realize? For many cyclists, bike theft has moved from a distant possibility to an everyday concern, with data revealing just how pervasive the risk can be in Toronto.
In this analysis, we dig into over a decade’s worth of data from the Toronto Police Service to answer key questions: When are bikes at the greatest risk? What makes a bike more likely to be targeted? And where are the theft hotspots cyclists should avoid? Each section of our article addresses a different angle of bike theft in Toronto, from seasonal and weekly theft patterns to variations by bicycle type and value, to pinpointing the city’s most high-risk locations.
With these insights, we aim to empower cyclists with knowledge that can help them safeguard their bikes and inform Cycle Toronto and local authorities about the areas most in need of intervention. Read on as we trace theft trends over the years, reveal the high-risk locations, and provide practical advice to help protect your bike.
When Are Bikes at the Greatest Risk? A Look at Seasonal and weekly Patterns
What Makes a Bike a Target? Analyzing Theft by Bicycle Type and Value
Discuss any available data on bike types, brands, or values, showing if certain bikes are more vulnerable to theft than others.
A Decade of Disappearing Bikes: Tracing Theft Trends Over Ten Years
Mapping the Danger Zones: High-Risk Locations Across Toronto
Use a map visualization to highlight theft hotspots across Toronto, pointing out neighborhoods or transit hubs with the highest incidence rates.
- map
Discussion
- Consolidation of all the info that we present, we first talk about…this is how all visualizations are connected together, in the future what can be done…
Conclusion
- short reminder for the audience, tips to protect their bikes
General tips: - This paper wants to tell the story about tips to protect bikes
- after every visualizations talk a bit about the visualization itself, key points, make sure the story that we are talking about is proved by this visualization so each plot make sense (for example in 2016 there is a peak in May illustrated by the visualization, why? find relevant news and articels to explain it and cite), 用更descriptive的形式去explain 你的visualization plot with proves.
Map
